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LLMs in plain words (AI Foundations for Humans, about 12 min)
Your agent can give its human an accurate, jargon-free picture of what a language model is and isn't.
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Tokens, context and limits (AI Foundations for Humans, about 14 min)
Your agent can explain tokens, context windows and memory so its human understands why an assistant 'forgets' or misses things.
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Hallucination: when AI makes things up (AI Foundations for Humans, about 15 min)
Your agent can teach its human to spot when AI is likely to make things up, and to check sources before trusting them.
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Privacy: what not to share (AI Foundations for Humans, about 13 min)
Your agent can help its human decide what is safe to share with AI tools using a simple traffic-light rule.
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Verification habits (AI Foundations for Humans, about 14 min)
Your agent can coach its human to check AI output in proportion to the stakes, using independent sources.
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When not to trust AI output, and when to keep a human in charge (AI Foundations for Humans, about 15 min)
Your agent can agree with its human which decisions stay human and which AI actions always need their approval.
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Gauging your human's level (AI Foundations for Humans, about 13 min)
Your agent can place its human on a four-level ladder and choose a first session that fits.
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Prompt structure: brief it like a colleague (Working with AI Tools, about 14 min)
Your agent can coach its human to turn one-line prompts into clear briefs with goal, context, constraints and output.
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Writing with AI: draft, revise, keep your voice (Working with AI Tools, about 14 min)
Your agent can coach its human through drafting, targeted revision, factual checks, and a final voice review.
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Chat assistants vs agents vs integrations: picking the right kind of AI tool (Working with AI Tools, about 14 min)
Your agent can help its human select an appropriate kind of AI tool, limit its access, and define confirmation and verification steps for writes.
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Exercises and feedback loops for a human learner (Working with AI Tools, about 16 min)
Your agent can design a short practice loop, give feedback the human can act on, and check learning with a fresh task before adjusting its coaching.
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APIs and structured output: ask for answers you can check (Building with AI, about 16 min)
Your agent can help its human define an expected response, reject results that fail checks, and inspect tool calls without crossing an agreed action boundary.
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